RNA molecules are crucial in many biological processes, therefore, they have become potential targets for disease diagnosis and treatment. The design of small molecules that can target RNA structures is a promising approach, as they are tunable and easily taken up by cells. However, it can be challenging without knowing the RNA structure. In this short opinion letter three different examples will be discussed of research groups combining AI to predict the interactions between RNA molecules and small molecules to address the challenges in designing RNA-targeted ligands due to the difficulty in obtaining accurate RNA structures and the lack of understanding of binding kinetics.

Cardiovascular disease, the leading cause of mortality worldwide, is primarily caused by atherosclerosis, which is characterized by lipid and inflammatory cell accumulation in blood vessels and carotid intima thickening, among others. Although disease management has improved significantly, new therapeutic strategies focused on accelerating atherosclerosis regression must be developed. Atherosclerosis models mimicking in vivolike conditions provide essential information for research and new advances towards clinical application. Here a therapeutic strategy to improve cholesterol efflux has been developed based on a twostep administration of rHDL consisting on a first dose of antagomiR33a loaded rHDLs to induce ABCA1 transporter overexpression, followed by a second dose of DPPC rHDLs, which remove efficiently cholesterol from foam cells. A triplecell 2D atheroma plaque model reflecting the cellular complexity of atherosclerosis has been used to overcome the translational gap providing a suitable model to improve the efficiency of the nanoparticles in promoting cholesterol efflux. The results show that sequential administration of rHDL potentiates cholesterol efflux indicating that this approach might be used in vivo to target more efficiently atherosclerotic lesions and improve prognosis of the disease. Sequential targeting of foam cells with nanoparticles including a first antagomiR33a delivery by DPPC:CE:LPC rHDL followed by a second infusion of DPPC rHDL induces a potent cholesterol efflux from foam cells, which may overcome current technique barriers to promote clinical applications.

With the increasing awareness of environmental protection, the greening of flame retardants has
become an inevitable choice for flame retardant technology. The use of bio-based materials in nature
as a flame retardant meets the requirements of a green strategy, which not only mitigates the energy
crisis, but also does not cause environmental pollution.

The reports of cyanobacterial blooms and their impact on ecosystems and human health have increased in the last two decades, becoming of emerging concern for the World Health Organization (WHO). Most of these blooms are non-specific, although a few species showed more dominance, frequently, toxin-producing strains of Microcystis. Likewise, in Cuba, some cyanobacterial water blooms have been published, mainly focused on species composition and abundance, but studies do not approach both morphological and molecular analyses. Herein, we performed the characterization of an unprecedented mixed bloom in the Abreus reservoir using morphological features and molecular biomarkers. We detected high concentrations of some species of cyanobacteria, in addition to some groups of phytoplankton. Our results revealed Microcystis sp. as dominant species, followed by Sphaerospermopsis torques-reginae, being confirmed through molecular biomarker analyses, the presence of the 16S rRNA gene and Microcystis-specific 16S rRNA gene. Besides, the screening on cyanotoxin genes, revealed the gene mycE, which is involved in the biosynthesis of microcystins. This study constitutes one the few records of non-specific Harmful Algal Blooms (HABs) in Cuba, based on both morphological and molecular level. Although this study shows unpublished data, we consider this work as an extended version of the article “Valle-Pombrol A., et al., Planktonic cyanobacteria from the Abreus Reservoir, Cienfuegos, Cuba. Pan-American Journal of Aquatic Sciences (2021), 16(1): 20-29; http://panamjas.org/pdf_artigos/PANAMJAS_16(1)_20-29.pdf”. Indeed, our findings reinforce the importance of monitoring program of cyanobacteria in reservoir waters used for agricultural activities, animals, and human consumption.

Computational methods have gone through enormous improvements during the recent years. In this context, different enhancements in chemoinformatic and anticancer drug predictions will be listed as follow.

High-throughput screening (HTS), as one of the key techniques in drug discovery, is frequently
used to identify promising drug candidates in a largely automated and cost-effective way. In this report
different methods and applications of HTS will be commented.

In the last four decades, nanotechnology has gained a lot of importance with no sign of slowing
down. Nanoparticles have made it possible to significantly extend the shelf lives of food product,
improve intracellular delivery of hydrophobic drugs and so on. The improvement that nanotechnology
has reached in the field of anticancer agents and new drug delivery systems will be seen.

Alzheimer's disease (AD), the most common type of dementia in older people, causes
neurological problems associated with memory and thinking. Different computational analysis could
act as useful tools in gaining more information about the development of the disease or potential target
drugs. That is why different computational methods will be reviewed.

This paper presents a preliminary study of the relationship between IoT and cyber security in the context of predictive data analytics. The study examines the key security challenges associated with IoT devices and the measures that can be taken to mitigate these risks. The paper also explores the role of predictive data analytics in managing and analyzing data collected by IoT devices and the potential benefits and challenges of this approach. The paper further explores the potential benefits of using predictive data analytics in managing and analyzing data collected by IoT devices. These benefits include the ability to identify patterns and trends in data, optimize resource allocation, and improve decision-making. However, the study also identifies challenges associated with the use of predictive data analytics, such as the need for high-quality data, the complexity of analytics algorithms, and the potential for bias and discrimination. Overall, the paper highlights the importance of addressing cyber security challenges associated with IoT devices in the context of predictive data analytics. The study emphasizes the need for comprehensive security measures to be implemented to protect IoT devices and the data they collect. The paper also highlights the potential benefits of using predictive data analytics in managing and analyzing IoT data but cautions that careful consideration of the associated risks and challenges is necessary. Finally, the study identifies areas for future research, such as the development of more effective security measures and the exploration of new data analytics algorithms and techniques.

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Terpenes are hydrocarbons from secondary metabolism of plants, which have known biological activities (e.g., antimicrobial, antioxidants, chemotherapy, and immunomodulatory agents). However, it is necessary to better explore its effects on neuropsychological disorders. This study presents a brief overview of the neuropsychotropic action of terpenes, based on a narrative review using a qualitative descriptive approach. Among the articles found, the most studied applications were for ischemia, seizures, Alzheimer's disease, multiple sclerosis, depression, and anxiety. The action of these terpenes against diseases such as Amyotrophic Lateral Sclerosis, Parkinson's and Huntington's Disease is still a little explored field that needs further studies.